Today's digitally controlled or Digital Signal Processing (DSP) hearing instruments or aids are often provided with a number of preset listening programs or preset programs. These preset programs are often included to accommodate comfortable and intelligible reproduced sound quality in differing listening environments. Audio signals obtained from these listening environments may possess very different characteristics, e.g. in terms of average and maximum sound pressure levels (SPLs) and/or frequency content. Therefore, for DSP based hearing prostheses, each type of listening environment may be associated with a particular preset program wherein a particular setting of algorithm parameters of a signal processing algorithm of the hearing prosthesis to ensure that the user is provided with an optimum reproduced signal quality in all types of listening environments. Algorithm parameters that typically could be adjusted from one listening program to another include parameters related to broadband gain, corner frequencies or slopes of frequency-selective filter algorithms and parameters controlling e.g. knee-points and compression ratios of Automatic Gain Control (AGC) algorithms.
Consequently, today's DSP based hearing instruments are usually provided with a number of different preset programs, each program tailored to a particular listening environment category and/or particular user preferences. Signal processing characteristics of each of these preset programs is typically determined during an initial fitting session in a dispenser's office and programmed into the instrument by transmitting or activating corresponding algorithms and algorithm parameters to a non-volatile memory area of the hearing prosthesis.
The hearing aid user is subsequently left with the task of manually selecting, typically by actuating a push-button on the hearing aid or a program button on a remote control, between the preset programs in accordance with his current listening or sound environment. Accordingly, when attending and leaving various sound environments in his/hers daily whereabouts, the hearing aid user may have to devote his attention to delivered sound quality and continuously search for the best preset program setting in terms of comfortable sound quality and/or the best speech intelligibility.
It would therefore be highly desirable to provide a hearing prosthesis such as a hearing aid or cochlea implant device that was capable of automatically classifying the user's listening environment so as to belong to one of a number of relevant or typical everyday listening environment categories. Thereafter, obtained classification results could be utilised in the hearing prosthesis to allow the device to automatically adjust signal processing characteristics of a selected preset program, or to automatically switch to another more suitable preset program. Such a hearing prosthesis will be able to maintain optimum sound quality and/or speech intelligibility for the individual hearing aid user across a range of differing and relevant listening environments.
In the past there have been made attempts to adapt signal processing characteristics of a hearing aid to the type of acoustic signals that the aid receives. U.S. Pat. No. 5,687,241 discloses a multi-channel DSP based hearing instrument that utilises continuous determination or calculation of one or several percentile value of input signal amplitude distributions to discriminate between speech and noise input signals. Gain values in each of a number of frequency channels is altered in response to detected levels of speech and noise. However, it is often desirable to provide a more fine-grained characterisation of a listening environment than only discriminating between speech and noise. As an example, it may be desirable to switch between an omni-directional and a directional microphone preset program in dependence of, not just the level of background noise, but also on further signal characteristics of this background noise. In situations where the user of the hearing prosthesis communicates with another individual in the presence of the background noise, it would be beneficial if it was possible to identify and classify the type of background noise. Omni-directional operation could be selected in the event that the noise being traffic noise to allow the user to clearly hear approaching traffic independent of its direction of arrival. If, on the other hand, the background noise was classified as being babble-noise, the directional listening program could be selected to allow the user to hear a target speech signal with improved signal-to-noise ratio (SNR) during a conversation.
A detailed characterisation of e.g. a microphone signal may be obtained by applying Hidden Markov Models for analysis and classification of the microphone signal. Hidden Markov Models are capable of modelling stochastic and non-stationary signals in terms of both short and long time temporal variations. Hidden Markov Models have been applied in speech recognition as a tool for modelling statistical properties of speech signals. The article “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, published in Proceedings of the IEEE, VOL 77, No. 2, February 1989 contains a comprehensive description of the application of Hidden Markov Models to problems in speech recognition.
The present applicants have, however, for the first time applied Hidden Markov Models to classify the listening environment of a hearing prosthesis. According to one aspect of the invention, classification results are utilised to support automatic parameter adjustment of a parameter or parameters of a predetermined signal processing algorithm executed by processing means of the hearing prosthesis. According to another aspect of the invention, features vectors extracted from a digital input signal of the hearing prostheses and processed by the Hidden Markov Models represent substantially level and/or absolute spectrum shape independent signal features of the digital input signal. This level independent property of the extracted features vectors provides robust classification results in real-life acoustic environments.